This study examines the role of blockchain-based smart contracts' influence on financial transparency and effectiveness in the economic activities of the emerging markets. In this study, the researchers utilised a mixed-method approach that includes a systematic literature review, comparative case studies from Africa, Southeast Asia, and Latin America, and expert interviews. The research findings evidence that the adoption of smart contracts can lower transaction costs, eliminate intermediary services, improve trust in financial systems, and serve as alternatives to the current financial systems. The results further demonstrate that smart contracts can improve financial inclusion through low-cost microfinance, insurance, and trade finance solutions, as well as enhance trust and transparency with immutable records and real-time auditing. Nevertheless, weaknesses in infrastructure, digital literacy, and regulatory uncertainty create difficulties for adoption. In addition, the study augments the existing prior research emphasising the impacts of financial technology innovation in emerging markets by offering findings that are beneficial to the market stakeholders including policymakers, financial services institutions, and technology innovators, by effectively positioning blockchain-based solutions implementation as better and viable option that can drive inclusive financial development in the emerging economies.
The study aims to examine how blockchain is used in the multimodal and interdisciplinary metaverse as a nexus of education and training, accounting, banking and finance, entertainment and media, marketing and e-commerce, and retail, healthcare, and wellness. It seeks to evaluate the influence of blockchain combined with artificial intelligence, the Internet of Things, and other emerging technologies in the metaverse, so evaluating the challenges and concerns in the field, new business prospects, and sustainable development paths corresponding to Sustainable Development Goals. Using a Systematic Literature Review (SLR) technique, this article addresses the problem from a commercial viewpoint. The study investigates the topic structure of the literature by means of Biblioshiny for R combined with VOSviewer version 1.6.20. Furthermore, increasing the analytical depth is a bibliographic coupling method used on a dataset of 172 Scopus (2024) items. The study underlines the economic rationality and direct consequences of the characteristics of the blockchain—NFTs, DeFi, cryptocurrencies, transparency, decentralization, and security—on the relevance of business models in the metaverse. The information provided here is a vital literature study on how the blockchain addresses issues in constructing and metamorphosing the metaverse from several sectors and angles. In the framework of the metaverse, this article offers a thorough theoretical study of the possibilities and possible challenges in blockchain integration.
Abstract: The transition from centralized digital ecosystems to decentralized, trust - driven architectures represents a defining paradigm shift in Customer Experience (CX). This paper presents a strategic blueprint for leveraging block chain technologies to build secure, transparent, and interoperable customer - centric environments between 2025 and 2030. Through a comprehensive review of market forecasts, enterprise case studies, and emerging regulatory frameworks, the study demonstrates how decentralized identity (DID), verifiable credentials, and tokenized loyalty systems fundamentally reshape customer engagement, ownership of personal data, and trust models. Findings indicate that block chain adoption empowers customers with self - sovereign identity control, enhances privacy compliance, and delivers measurable efficiency gains in verification, loyalty management, and supply - chain transparency. Case evidence from leading enterprises — including JPMorgan, AXA, Santander, and Accenture — highlights significant improvements in transaction speed, operational costs, and customer engagement. Despite challenges such as legacy system integration and GDPR - related constraints, hybrid architectures, Layer - Two scalability, and permissioned block chain environments provide viable adoption pathways. This paper concludes that block chain is not a supplementary technology for CX, but a foundational enabler of decentralized trust, competitive differentiation, and customer - driven digital ecosystems. Keywords: Block chain; Customer Experience (CX), Decentralized Identity (DID), Verifiable Credentials, Tokenized Loyalty Programs, Digital Trust, Self - Sovereign Identity, Smart Contracts, Hybrid Data Architecture, GDPR Compliance, Enterprise Digital Transformation, Web3 Customer Strategy
The global carbon credit trading market faces significant challenges including lack of real-time verification, double-spending issues, and insufficient transparency in emission measurements. This paper presents a novel blockchain-enabled framework integrating Internet of Things (IoT) sensors for automated carbon credit generation and trading. Our proposed system combines tamper-proof IoT sensor networks with smart contract automation to address current limitations in carbon credit systems. The methodology employs distributed sensor nodes equipped with CO2, temperature, and humidity sensors connected to an Ethereum-based blockchain network. Through extensive simulation and real-world testing, our system demonstrates 99.2% accuracy in emission measurement and real-time carbon credit generation. The framework reduces verification time by 87% compared to traditional manual verification processes while ensuring immutable transaction records. Key contributions include: (1) a decentralized IoT-blockchain architecture for carbon monitoring, (2) smart contract protocols for automated credit generation, (3) a novel consensus mechanism for sensor data validation, and (4) comprehensive security analysis demonstrating resistance to common blockchain attacks. Results indicate significant potential for transforming carbon credit markets through enhanced transparency, reduced fraud, and improved environmental monitoring accuracy.
The article examines the economic and organizational efficiency of implementing smart contracts based on blockchain technology in the public procurement system of the construction sector of the Russian Federation and St. Petersburg. Relevance research conditioned by the need to increase transparency, reduce transaction and administrative costs, and speed up procurement procedures in the context of large-scale public investment and limited budget resources. The paper develops a methodology for quantitatively assessing the economic effect of using smart contracts, including an analysis of direct savings in budget funds, reduced procurement processing time, and increased capital turnover. Based on official statistics and economic and mathematical modeling, it is shown that the introduction of smart contracts can reduce costs by 10% of the total volume of purchases, which is equivalent to savings of about 550 billion rubles for the Russian Federation and 68.2 billion rubles for St. Petersburg. Additional savings are achieved by reducing the average procurement processing time from 15 to 10 days, which leads to a decrease in administrative costs by 8.15 billion rubles and 1.13 billion rubles, respectively. A comprehensive assessment of the total economic effect confirms the high feasibility of digitalizing procurement procedures using smart contracts, which can become the basis for further transformation of the public finance management system and increasing the efficiency of using budget funds in the construction industry.
Smart contracts are integral to blockchain technology, enabling decentralized and automated transactions. This study examines 1,000 smart contracts by analyzing metrics such as total transactions, unique users, total value transferred (ETH), gas consumption, and call frequency. Total transactions range from 1 to 18,902, with unique users spanning 1 to 14,839. The average total value transferred is 3,245.87 ETH, peaking at 7,850.16 ETH, while gas consumption averages 25,486,392 units with a maximum of 58,471,065 units. Strong correlations were identified between transaction volume (r = 0.78), user engagement, and gas consumption. Clustering analysis categorizes contracts into low, moderate, and high-activity groups, while anomaly detection highlights 32 contracts with unusual behaviors, indicating inefficiencies or vulnerabilities. These findings emphasize the importance of optimizing smart contract designs to improve efficiency, security, and scalability. The study provides actionable insights into operational patterns and proposes future research directions, including design optimization, real-time monitoring, cross-platform analysis, and machine learning applications for predictive modeling. By addressing these aspects, this research contributes to the ongoing development of robust and efficient decentralized systems.
Effective data governance is crucial in modern digital ecosystems, ensuring secure, transparent, and efficient data sharing. Traditional centralized governance models often suffer from trust issues, inefficiencies, and security vulnerabilities. Blockchain technology offers a decentralized and tamper-resistant solution to address these challenges. This paper proposes a blockchain-based data governance architecture that enhances data sharing mechanisms and optimizes smart contract execution. The framework leverages a permissioned blockchain to ensure controlled data access while maintaining data integrity and security. To further improve performance, an optimized smart contract mechanism is introduced using gas-efficient transaction designs and layer-2 scaling solutions. Experimental evaluations demonstrate that the proposed model improves transaction efficiency, reduces computational overhead, and enhances security compared to conventional blockchain-based governance systems. The results highlight the potential of blockchain in establishing a decentralized, efficient, and transparent data governance framework for secure and scalable data exchange.
Amid the ongoing advancements associated with the Fourth Industrial Revolution and the intensification of digital transformation, the deployment of artificial intelligence (AI) within the banking sector has become an inevitable trajectory, enabling substantial innovations in financial management and operational processes. AI technologies facilitate the automation of complex workflows, reduce error rates, enhance operational efficiency, and improve customer experience through personalized services and accelerated response mechanisms. Applications span various functions, including customer onboarding, service delivery, product development, marketing, and risk management, thereby optimizing the banking value chain holistically. Moreover, AI’s capabilities in big data analytics and customer behavior prediction equip financial institutions with more robust decision-making tools that mitigate credit risk and fraud incidence. The convergence of AI and blockchain technologies further augments transaction security and transparency, thereby promoting the expansion of digital banking and decentralized finance ecosystems. This study aims to systematically examine the evolving roles and emerging applications of AI throughout the banking value chain, contributing to strategic frameworks oriented toward sustainable development within the digital era.
Cryptocurrency markets are experiencing rapid growth, but this expansion comes with significant challenges, particularly in predicting cryptocurrency prices for traders in the U.S. In this study, we explore how deep learning and machine learning models can be used to forecast the closing prices of the XRP/USDT trading pair. While many existing cryptocurrency prediction models focus solely on price and volume patterns, they often overlook market liquidity, a crucial factor in price predictability. To address this, we introduce two important liquidity proxy metrics: the Volume-To-Volatility Ratio (VVR) and the Volume-Weighted Average Price (VWAP). These metrics provide a clearer understanding of market stability and liquidity, ultimately enhancing the accuracy of our price predictions. We developed four machine learning models, Linear Regression, Random Forest, XGBoost, and LSTM neural networks, using historical data without incorporating the liquidity proxy metrics, and evaluated their performance. We then retrained the models, including the liquidity proxy metrics, and reassessed their performance. In both cases (with and without the liquidity proxies), the LSTM model consistently outperformed the others. These results underscore the importance of considering market liquidity when predicting cryptocurrency closing prices. Therefore, incorporating these liquidity metrics is essential for more accurate forecasting models. Our findings offer valuable insights for traders and developers seeking to create smarter and more risk-aware strategies in the U.S. digital assets market.
This study aims to analyze and compare the performance of three major cryptocurrencies—Bitcoin, Ethereum, and Solana—during the 2021–2024 period, based on return, risk, and risk-adjusted performance indicators (Sharpe Ratio). The research applies a comparative quantitative method using secondary data from CoinMarketCap. The analysis includes descriptive statistics, annual return calculations, standard deviation, Value at Risk (VaR), Expected Shortfall (ES), and Sharpe Ratio. ANOVA was used to test differences in return, while Kruskal-Wallis and Mann-Whitney U tests were employed for risk and performance due to non-normal data distributions. The results show no significant differences in average return among the three assets. However, there are significant differences in risk levels, with Solana being the most volatile, followed by Ethereum and Bitcoin. In terms of Sharpe Ratio, no significant difference in performance was found. These findings indicate that while there are absolute differences in return and risk, the three assets provide a balanced level of return when adjusted for risk. Hence, diversification among these assets may serve as a relevant strategy for investors depending on their risk profiles.
Yangchun Xiong, Li Ding, Shu Guo, Tsan‐Ming Choi · 5 authors
ABSTRACT Smart contracts, enabled by blockchain technology, are increasingly adopted by firms to automate the execution of agreements or contracts without the involvement of intermediaries. However, it is still unclear how smart contracts may affect firms' operational efficiency. We address this issue empirically by conducting a quasi‐natural experiment in the United States in which certain states have enacted relevant laws that increase in‐state firms' propensity to adopt and use smart contracts. Our difference‐in‐differences estimation suggests that compared with out‐of‐state control firms, in‐state treatment firms' operational efficiency increases significantly after the enactment of smart contract laws. Our post hoc analysis further suggests that state‐level smart contract laws help increase in‐state firms' actual smart contract activities, which in turn lead to operational efficiency improvement. We also find that the operational efficiency improvement varies across firms with different supply chain complexities. While firms with a large number of supply chain partners (i.e., high horizontal complexity) gain more operational efficiency improvement, the improvement becomes less pronounced if firms' supply chain partners are distributed across different countries (i.e., high spatial complexity). Overall, our research not only demonstrates smart contracts' ability to improve operational efficiency but also reveals the critical role of supply chain complexity in affecting the operational efficiency improvement.
Joel Sepúlveda, Amanda Lemette, Karla Ohler-Martins
The rise of cryptocurrencies and decentralised finance (DeFi) has fuelled a fast-growing digital assets economy with major environmental and financial implications. Proof-of-work (PoW) systems like Bitcoin demand high energy and emit large volumes of CO₂, while proof-of-stake (PoS) alternatives such as Ethereum and Cardano significantly reduce environmental costs. This paper analyses seven major crypto projects: Ethereum, Uniswap, Aave, Maker, Cardano, XRP, and Stellar. It focuses on their energy consumption, financial performance, and sustainability. The study proposes a novel sustainability scoring framework to support ESG-aligned investment and regulatory design. While PoW offers unmatched security, its environmental toll is unsustainable. PoS models show promise but face governance and scalability concerns. The study highlights the urgent need for sustainable innovation and regulatory differentiation to align crypto markets with climate goals, investor expectations, and long-term economic viability.
The present study aims to analyze the return and risk performance ofselected cryptocurrencies in orderto find out which cryptocurrencies have small risks and large returns. The research time period is 2017 to 2022. The objective of this research is to compute and compare the risk and return performance of the selected cryptos. The findings of this research are that the risk is very high in Bitcoin compared to Ethereum, as shown in the data analysis, and Ethereum has high returns. Before starting an investment, it is better to look at the ability of Cryptocurrency assets to minimize risks and make sure that the investment objectives are for the long and short term.
Smart cities present a transformative paradigm for urban development, yet securing sustainable financing remains a critical challenge. While traditional funding mechanisms struggle with scalability limitations, FinTech innovations like Initial Coin Offerings (ICOs) have emerged as a viable alternative. Leveraging blockchain technology, ICOs enable decentralized capital raising through token sales, offering transparency and global investor access. However, their effectiveness is compromised by market volatility, information asymmetry, and the absence of reliable predictive frameworks. This study addresses these limitations by developing an explainable hybrid machine learning model that combines: (1) Light Gradient Boosting Machine (LGBM) for efficient feature selection through histogram-based learning, (2) Optuna-optimized Extremely Randomized Trees regression that mitigates overfitting via enhanced randomization while excelling with noisy financial data, and (3) interpretability tools including SHAP values and feature importance analysis. Optuna's automated hyperparameter optimization further enhances computational efficiency, enabling robust predictions of post-ICO returns. The proposed model demonstrates superior predictive performance (R²=0.814, MSE=0.005, MAE=0.051), significantly outperforming both linear regression and state-of-the-art ML models. Key findings identify token supply (63% predictive power) as negatively correlated with returns - reflecting dilution effects and investor perceptions of scarcity- while fundraising success (15%) and Bitcoin returns (8%) show positive influences. These results provide practical guidance for investors and regulators, while establishing ICOs as a potential sustainable financing mechanism for smart city initiatives. The study contributes both methodologically through its optimized hybrid architecture and practically by enhancing decision-making in blockchain-based urban development financing.
Integrating blockchain into healthcare devices offers potential for improved data control but faces significant usability and acceptance challenges. This study addresses this gap by evaluating CipherPal, an improved blockchain-enabled smart fidget toy prototype, using a multi-framework approach to understand the interplay between technology, design, and user experience. We combined insights from an expert review assessing adherence to Web3 Design Guidelines, a User Acceptance Toolkit assessment with professionals based on UTAUT2, and extended user testing over three days. Findings revealed that users valued CipherPal's satisfying tactile interaction and perceived benefits for well-being, such as stress relief. However, significant usability barriers emerged, primarily related to challenging device-application connectivity, data synchronization, and disruptive physical elements. While conceptually accepted, the blockchain integration mainly added interaction friction and complexity, overshadowing its potential benefits for users during the study. The multi-framework approach proved valuable, providing complementary insights and highlighting tensions between the device's core appeal and usability challenges. This research underscores the critical need for user-centered design in blockchain health applications, emphasizing seamless usability, abstracting technical complexity, and holistically considering physical and social factors.
The evolution of the metaversea collective virtual environment with shared immersion that includes virtual reality (VR), augmented reality (AR), blockchain, and internet technologieshas created new entrepreneurial opportunities, particularly in virtual real estate. Metaverse real estate is blockchain-backed virtual land parcels that can be bought, sold, developed, and rented out in virtual worlds such as Decentraland, The Sandbox, and others. Unlike traditional physical property, ownership in the metaverse is guaranteed through non-fungible tokens (NFTs) to enable transparent and irrevocable proof of ownership. The new digital asset class has created novel entrepreneurial opportunities, including property development, virtual renting, event planning, advertising, and real estate services for virtual properties.Immersive technologies are utilized by entrepreneurs here to develop interactive 3D environments, so it is now possible to provide customers with experiences that are not limited by geography and physics. Virtual real estate development involves building interactive digital properties such as virtual offices, malls, galleries, and entertainment hubs, which can be commercialized via rentals, ticketing, sponsorships, and advertising. Early adopters have realized significant returns on investments, with some virtual plots appreciating by over 500% within months, underscoring the lucrative potential of this emerging market. The metaverse also fosters a democratized and inclusive entrepreneurial ecosystem by lowering classical entry barriers. Virtual businesses require less physical infrastructure and less up-front investment, and entrepreneurs can experiment, prototype, and test business models with less capital exposure. Moreover, the global connectedness of the metaverse enables collaboration across heterogeneous expertise and markets, accelerating innovation and business scaling. This review paper integrates available scientific literature and scrutinizes entrepreneurship in metaverse real estate in terms of market dynamics, technological underpinnings, entrepreneurial strategies, challenges, and opportunities
In recent years, cutting-edge technologies, such as artificial intelligence (AI), blockchain, and digital twin (DT), have revolutionized the healthcare sector by enhancing public health and treatment quality through precise diagnosis, preventive measures, and real-time care capabilities. Despite these advancements, the massive amount of generated biomedical data puts substantial challenges associated with information security, privacy, and scalability. Applying blockchain in healthcare-based digital twins ensures data integrity, immutability, consistency, and security, making it a critical component in addressing these challenges. Federated learning (FL) has also emerged as a promising AI technique to enhance privacy and enable decentralized data processing. This paper investigates the integration of digital twin concepts with blockchain and FL in the healthcare domain, focusing on their architecture and applications. It also explores platforms and solutions that leverage these technologies for secure and scalable medical implementations. A case study on federated learning for electroencephalogram (EEG) signal classification is presented, demonstrating its potential as a diagnostic tool for brain activity analysis and neurological disorder detection. Finally, we highlight the key challenges, emerging opportunities, and future directions in advancing healthcare digital twins with blockchain and federated learning, paving the way for a more intelligent, secure, and privacy-preserving medical ecosystem.
Статията изследва въздействието на новата институционална икономика (НИЕ) върху аграрния сектор, като акцентира върху ролята на смарт договорите за намаляване на транзакционните разходи и повишаване на икономическата ефективност. НИЕ разглежда институциите като ключов фактор за координация и управление на икономическите взаимодействия, особено в условия на висока несигурност, специфичност на активите и опортюнистично поведение. Смарт договорите, базирани на блокчейн технология, се представят като иновативен механизъм за автоматизиране на процесите на договаряне, мониторинг и изпълнение на споразумения. Те елиминират необходимостта от посредници, минимизират риска от човешки грешки и увеличават прозрачността във веригата на стойността. Използвайки стохастични модели, статията оценява ефективността на разходите за преки вложения, договорна работа и обслужване на дълга при три сценария – базов, оптимистичен и песимистичен. Резултатите показват значителен потенциал на смарт договорите да трансформират аграрния сектор чрез намаляване на транзакционните разходи и подобряване на рентабилността, особено при пълно внедряване. Въпреки това, приложението им е ограничено от недостатъчно развита инфраструктура, липса на технологични умения и неясноти в регулаторната рамка. Статията подчертава необходимостта от целенасочена институционална подкрепа за преодоляване на тези бариери. Изследването допринася към литературата с интердисциплинарен подход, свързващ теоретични модели и практически анализи, предоставяйки основа за развитие на устойчиви политики в аграрния сектор.
Based on the document content, I'll create a comprehensive abstract that captures the key aspects of the research. The research investigates the performance and efficiency of various consumer banking platforms using Grey Relational Analysis (GRA). The study analyzed five distinct banking platforms—Online Banks (Nedbank's), Credit Unions, Peer-to-Peer (P2P) Lending, Fintech Solutions, and Cryptocurrency/Decentralized Finance (Deify)—across four critical dimensions: Customer Satisfaction, Digital Banking and Technology, Financial Products and Services, and Customer Support. The analysis employed normalized data, deviation sequences, and grey relation coefficients to establish comprehensive performance metrics. The findings reveal significant variations in platform effectiveness, with Fintech solutions achieving the highest Grey Relationship Grade (GRG: 0.7387), followed closely by P2P lending (GRG: 0.7064). Traditional platforms like Credit Unions maintained moderate performance (GRG: 0.5674), while Cryptocurrency/Deify (GRG: 0.5117) and Online Banks (GRG: 0.5115) showed considerable room for improvement. The research demonstrates that success in modern banking requires a balanced integration of technological innovation with customer-centric services, rather than excellence in isolated areas. These results hold significant importance for shaping the strategic growth of banking services and guiding the future advancement of financial technology platforms.
Bitcoin has attained increasing recognition and interest from individuals and corporations, with more than $1 billion market capitalization. Twitter users’ sentiment on the topic is a major factor that influences volatility of Bitcoin’s price. Compared to other financial markets, there are a limited number of studies that discuss the price fluctuation prediction of Bitcoin using Twitter sentiment. A dataset with 16 million tweets from August 2018 to October 2019 was utilized for finding the correlation between the daily close price of Bitcoin and Twitter sentiment. This dataset was pre-processed by following steps such as removing null, duplicate and non-English tweets. The sentiment analysis was carried out using VADER sentiment analyzer. This research utilized hyperparameter optimization and improved two deep learning models (with Long Short-Term Memory and Convolutional Neural Network architectures), for the tasks of direction and magnitude prediction with accuracies of 82.35% and 72.06%, respectively on test datasets. With hyperparameter optimization this research addresses a gap in the existing research of this research area, which was not utilizing hyperparameter optimization to improve deep learning models.
Najma Ali Soomro, Suresh Kumar Oad RAJPUT, Ishfaque Ahmed
Predictions regarding returns and price movements in financial markets can be made using online search engines, which track the sentiments of individual investors. This study aims to analyse how the sentiments of Bitcoin investors impact changes in the American stock market returns. The Bitcoin sentiment index was created to benchmark the sentiments of Bitcoin investors from 2013 to 2018. This index is built by analysing terms from leading business magazines and online journals. Such an index measures potential investors’ sentiments about Bitcoin and how those sentiments impact S&P returns. We use the ordinary least squares method to analyse this. It was found that BSI has a negative impact on S&P returns. Furthermore, the Vector Autoregressive (VAR) model is used to determine the relationship between these economic time series. VAR results indicated a significant positive impact of S&P returns on BSI, while BSI could not predict S&P returns. Consequently, it can be concluded that S&P returns cause changes in BSI. Recognising that Bitcoin sentiment can offer valuable insights and guidance for retail investors during market downturns, much like the S&P 500. By tracking changes in the S&P 500, analysts can anticipate shifts in cryptocurrency market sentiment and take preventative measures when needed. Understanding this relationship is crucial for assessing systemic risks, as volatility in traditional markets can impact the crypto space.
Andrea Bongini, Marco Sparacino, Luca Marzi, Carlo Biagini
In recent years, Facility Management has undergone significant technological and methodological advancements, primarily driven by Building Information Modelling (BIM), Computer-Aided Facility Management (CAFM), and Computerized Maintenance Management Systems (CMMS). These innovations have improved process efficiency and risk management. However, challenges remain in asset management, maintenance, traceability, and transparency. This study investigates the potential of blockchain technology and non-fungible tokens (NFTs) to address these challenges. By referencing international (ISO, BOMA) and European (EN) standards, the research develops an asset management process model incorporating blockchain and NFTs. The methodology includes evaluating the technical and practical aspects of this model and strategies for metadata utilization. The model ensures an immutable record of transactions and maintenance activities, reducing errors and fraud. Smart contracts automate sub-phases like progress validation and milestone-based payments, increasing operational efficiency. The study’s practical implications are significant, offering advanced solutions for transparent, efficient, and secure Facility Management. It lays the groundwork for future research, emphasizing practical implementations and real-world case studies. Additionally, integrating blockchain with emerging technologies like artificial intelligence and machine learning could further enhance Facility Management processes.